Image Classification
Keras
LiteRT
Spanish
agriculture
plant-disease
soybean
computer-vision
tensorflow-lite
edge-ai
Instructions to use alejandroramirezucb/glycine-vision-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use alejandroramirezucb/glycine-vision-models with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://alejandroramirezucb/glycine-vision-models") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edea57dc7865a74a5819073d0157b6e0b37412d563e01d87ea1e136bf6974939
- Size of remote file:
- 63.3 MB
- SHA256:
- f7865b02ecff28edce8608ac97ecaea5d221d5040ee921ec868dd84e45b91e02
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